Overview

Dataset statistics

Number of variables13
Number of observations569
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory57.9 KiB
Average record size in memory104.2 B

Variable types

Numeric13

Alerts

mean radius is highly overall correlated with radius errorHigh correlation
mean smoothness is highly overall correlated with mean compactness and 2 other fieldsHigh correlation
mean compactness is highly overall correlated with mean smoothness and 4 other fieldsHigh correlation
mean symmetry is highly overall correlated with mean smoothness and 2 other fieldsHigh correlation
mean fractal dimension is highly overall correlated with mean smoothnessHigh correlation
radius error is highly overall correlated with mean radius and 2 other fieldsHigh correlation
compactness error is highly overall correlated with mean compactness and 1 other fieldsHigh correlation
concave points error is highly overall correlated with mean compactness and 2 other fieldsHigh correlation
worst symmetry is highly overall correlated with mean symmetryHigh correlation
concave points error has 13 (2.3%) zerosZeros

Reproduction

Analysis started2023-05-03 19:09:16.435624
Analysis finished2023-05-03 19:10:06.604129
Duration50.17 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

mean radius
Real number (ℝ)

Distinct456
Distinct (%)80.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.127292
Minimum6.981
Maximum28.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T19:10:06.765982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6.981
5-th percentile9.5292
Q111.7
median13.37
Q315.78
95-th percentile20.576
Maximum28.11
Range21.129
Interquartile range (IQR)4.08

Descriptive statistics

Standard deviation3.5240488
Coefficient of variation (CV)0.24944971
Kurtosis0.84552162
Mean14.127292
Median Absolute Deviation (MAD)1.9
Skewness0.94237957
Sum8038.429
Variance12.41892
MonotonicityNot monotonic
2023-05-03T19:10:07.014273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.34 4
 
0.7%
11.71 3
 
0.5%
12.46 3
 
0.5%
13.05 3
 
0.5%
10.26 3
 
0.5%
13.85 3
 
0.5%
12.77 3
 
0.5%
13.17 3
 
0.5%
13 3
 
0.5%
15.46 3
 
0.5%
Other values (446) 538
94.6%
ValueCountFrequency (%)
6.981 1
0.2%
7.691 1
0.2%
7.729 1
0.2%
7.76 1
0.2%
8.196 1
0.2%
8.219 1
0.2%
8.571 1
0.2%
8.597 1
0.2%
8.598 1
0.2%
8.618 1
0.2%
ValueCountFrequency (%)
28.11 1
0.2%
27.42 1
0.2%
27.22 1
0.2%
25.73 1
0.2%
25.22 1
0.2%
24.63 1
0.2%
24.25 1
0.2%
23.51 1
0.2%
23.29 1
0.2%
23.27 1
0.2%

mean texture
Real number (ℝ)

Distinct479
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.289649
Minimum9.71
Maximum39.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T19:10:07.291236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum9.71
5-th percentile13.088
Q116.17
median18.84
Q321.8
95-th percentile27.15
Maximum39.28
Range29.57
Interquartile range (IQR)5.63

Descriptive statistics

Standard deviation4.3010358
Coefficient of variation (CV)0.22297118
Kurtosis0.75831897
Mean19.289649
Median Absolute Deviation (MAD)2.81
Skewness0.65044954
Sum10975.81
Variance18.498909
MonotonicityNot monotonic
2023-05-03T19:10:07.569164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.52 3
 
0.5%
16.85 3
 
0.5%
16.84 3
 
0.5%
19.83 3
 
0.5%
14.93 3
 
0.5%
17.46 3
 
0.5%
18.9 3
 
0.5%
15.7 3
 
0.5%
18.22 3
 
0.5%
20.22 2
 
0.4%
Other values (469) 540
94.9%
ValueCountFrequency (%)
9.71 1
0.2%
10.38 1
0.2%
10.72 1
0.2%
10.82 1
0.2%
10.89 1
0.2%
10.91 1
0.2%
10.94 1
0.2%
11.28 1
0.2%
11.79 1
0.2%
11.89 1
0.2%
ValueCountFrequency (%)
39.28 1
0.2%
33.81 1
0.2%
33.56 1
0.2%
32.47 1
0.2%
31.12 1
0.2%
30.72 1
0.2%
30.62 1
0.2%
29.97 1
0.2%
29.81 1
0.2%
29.43 1
0.2%

mean smoothness
Real number (ℝ)

Distinct474
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.096360281
Minimum0.05263
Maximum0.1634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T19:10:07.853806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.05263
5-th percentile0.075042
Q10.08637
median0.09587
Q30.1053
95-th percentile0.11878
Maximum0.1634
Range0.11077
Interquartile range (IQR)0.01893

Descriptive statistics

Standard deviation0.014064128
Coefficient of variation (CV)0.14595358
Kurtosis0.85597493
Mean0.096360281
Median Absolute Deviation (MAD)0.0095
Skewness0.45632376
Sum54.829
Variance0.0001977997
MonotonicityNot monotonic
2023-05-03T19:10:08.144620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1007 5
 
0.9%
0.115 4
 
0.7%
0.1054 4
 
0.7%
0.1075 4
 
0.7%
0.1063 3
 
0.5%
0.117 3
 
0.5%
0.1049 3
 
0.5%
0.1044 3
 
0.5%
0.1066 3
 
0.5%
0.1158 3
 
0.5%
Other values (464) 534
93.8%
ValueCountFrequency (%)
0.05263 1
0.2%
0.06251 1
0.2%
0.06429 1
0.2%
0.06576 1
0.2%
0.06613 1
0.2%
0.06828 1
0.2%
0.06883 1
0.2%
0.06935 1
0.2%
0.0695 1
0.2%
0.06955 1
0.2%
ValueCountFrequency (%)
0.1634 1
0.2%
0.1447 1
0.2%
0.1425 1
0.2%
0.1398 1
0.2%
0.1371 1
0.2%
0.1335 1
0.2%
0.1326 1
0.2%
0.1323 1
0.2%
0.1291 1
0.2%
0.1286 1
0.2%

mean compactness
Real number (ℝ)

Distinct537
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10434098
Minimum0.01938
Maximum0.3454
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T19:10:08.434061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.01938
5-th percentile0.04066
Q10.06492
median0.09263
Q30.1304
95-th percentile0.2087
Maximum0.3454
Range0.32602
Interquartile range (IQR)0.06548

Descriptive statistics

Standard deviation0.052812758
Coefficient of variation (CV)0.50615545
Kurtosis1.6501305
Mean0.10434098
Median Absolute Deviation (MAD)0.03263
Skewness1.190123
Sum59.37002
Variance0.0027891874
MonotonicityNot monotonic
2023-05-03T19:10:08.702738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1147 3
 
0.5%
0.1206 3
 
0.5%
0.07698 2
 
0.4%
0.05743 2
 
0.4%
0.03834 2
 
0.4%
0.1516 2
 
0.4%
0.1117 2
 
0.4%
0.1111 2
 
0.4%
0.2087 2
 
0.4%
0.1047 2
 
0.4%
Other values (527) 547
96.1%
ValueCountFrequency (%)
0.01938 1
0.2%
0.02344 1
0.2%
0.0265 1
0.2%
0.02675 1
0.2%
0.03116 1
0.2%
0.03212 1
0.2%
0.03393 1
0.2%
0.03398 1
0.2%
0.03454 1
0.2%
0.03515 1
0.2%
ValueCountFrequency (%)
0.3454 1
0.2%
0.3114 1
0.2%
0.2867 1
0.2%
0.2839 1
0.2%
0.2832 1
0.2%
0.2776 1
0.2%
0.277 1
0.2%
0.2768 1
0.2%
0.2665 1
0.2%
0.2576 1
0.2%

mean symmetry
Real number (ℝ)

Distinct432
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18116186
Minimum0.106
Maximum0.304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T19:10:09.009817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.106
5-th percentile0.1415
Q10.1619
median0.1792
Q30.1957
95-th percentile0.23072
Maximum0.304
Range0.198
Interquartile range (IQR)0.0338

Descriptive statistics

Standard deviation0.027414281
Coefficient of variation (CV)0.15132479
Kurtosis1.287933
Mean0.18116186
Median Absolute Deviation (MAD)0.0171
Skewness0.72560897
Sum103.0811
Variance0.00075154282
MonotonicityNot monotonic
2023-05-03T19:10:09.302692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1714 4
 
0.7%
0.1769 4
 
0.7%
0.1893 4
 
0.7%
0.1601 4
 
0.7%
0.1717 4
 
0.7%
0.1861 3
 
0.5%
0.1966 3
 
0.5%
0.1925 3
 
0.5%
0.1506 3
 
0.5%
0.1739 3
 
0.5%
Other values (422) 534
93.8%
ValueCountFrequency (%)
0.106 1
0.2%
0.1167 1
0.2%
0.1203 1
0.2%
0.1215 1
0.2%
0.122 1
0.2%
0.1274 1
0.2%
0.1305 1
0.2%
0.1308 1
0.2%
0.1337 1
0.2%
0.1339 1
0.2%
ValueCountFrequency (%)
0.304 1
0.2%
0.2906 1
0.2%
0.2743 1
0.2%
0.2678 1
0.2%
0.2655 1
0.2%
0.2597 1
0.2%
0.2595 1
0.2%
0.2569 1
0.2%
0.2556 1
0.2%
0.2548 1
0.2%

mean fractal dimension
Real number (ℝ)

Distinct499
Distinct (%)87.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06279761
Minimum0.04996
Maximum0.09744
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T19:10:09.601001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.04996
5-th percentile0.053926
Q10.0577
median0.06154
Q30.06612
95-th percentile0.07609
Maximum0.09744
Range0.04748
Interquartile range (IQR)0.00842

Descriptive statistics

Standard deviation0.0070603628
Coefficient of variation (CV)0.11243044
Kurtosis3.0058921
Mean0.06279761
Median Absolute Deviation (MAD)0.00422
Skewness1.3044888
Sum35.73184
Variance4.9848723 × 10-5
MonotonicityNot monotonic
2023-05-03T19:10:09.881193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.06113 3
 
0.5%
0.05913 3
 
0.5%
0.05907 3
 
0.5%
0.05667 3
 
0.5%
0.06782 3
 
0.5%
0.05866 2
 
0.4%
0.0602 2
 
0.4%
0.05674 2
 
0.4%
0.06412 2
 
0.4%
0.06019 2
 
0.4%
Other values (489) 544
95.6%
ValueCountFrequency (%)
0.04996 1
0.2%
0.05024 1
0.2%
0.05025 1
0.2%
0.05044 1
0.2%
0.05054 1
0.2%
0.05096 1
0.2%
0.05176 1
0.2%
0.05177 1
0.2%
0.05185 1
0.2%
0.05223 1
0.2%
ValueCountFrequency (%)
0.09744 1
0.2%
0.09575 1
0.2%
0.09502 1
0.2%
0.09296 1
0.2%
0.0898 1
0.2%
0.08743 1
0.2%
0.0845 1
0.2%
0.08261 1
0.2%
0.08243 1
0.2%
0.08142 1
0.2%

radius error
Real number (ℝ)

Distinct540
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40517206
Minimum0.1115
Maximum2.873
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T19:10:10.152842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1115
5-th percentile0.1601
Q10.2324
median0.3242
Q30.4789
95-th percentile0.95952
Maximum2.873
Range2.7615
Interquartile range (IQR)0.2465

Descriptive statistics

Standard deviation0.27731273
Coefficient of variation (CV)0.68443203
Kurtosis17.686726
Mean0.40517206
Median Absolute Deviation (MAD)0.106
Skewness3.0886122
Sum230.5429
Variance0.076902352
MonotonicityNot monotonic
2023-05-03T19:10:10.432269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.286 3
 
0.5%
0.2204 3
 
0.5%
0.2684 2
 
0.4%
0.2239 2
 
0.4%
0.1601 2
 
0.4%
0.2957 2
 
0.4%
0.2562 2
 
0.4%
0.163 2
 
0.4%
0.403 2
 
0.4%
0.306 2
 
0.4%
Other values (530) 547
96.1%
ValueCountFrequency (%)
0.1115 1
0.2%
0.1144 1
0.2%
0.1153 1
0.2%
0.1166 1
0.2%
0.1186 1
0.2%
0.1194 1
0.2%
0.1199 1
0.2%
0.121 1
0.2%
0.1267 1
0.2%
0.1302 1
0.2%
ValueCountFrequency (%)
2.873 1
0.2%
2.547 1
0.2%
1.509 1
0.2%
1.37 1
0.2%
1.296 1
0.2%
1.292 1
0.2%
1.291 1
0.2%
1.215 1
0.2%
1.214 1
0.2%
1.207 1
0.2%

texture error
Real number (ℝ)

Distinct519
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2168534
Minimum0.3602
Maximum4.885
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T19:10:10.735575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.3602
5-th percentile0.54014
Q10.8339
median1.108
Q31.474
95-th percentile2.212
Maximum4.885
Range4.5248
Interquartile range (IQR)0.6401

Descriptive statistics

Standard deviation0.55164839
Coefficient of variation (CV)0.45334005
Kurtosis5.3491687
Mean1.2168534
Median Absolute Deviation (MAD)0.3153
Skewness1.6464438
Sum692.3896
Variance0.30431595
MonotonicityNot monotonic
2023-05-03T19:10:11.002771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.15 3
 
0.5%
1.35 3
 
0.5%
1.268 3
 
0.5%
0.8561 3
 
0.5%
1.016 2
 
0.4%
1.194 2
 
0.4%
1.095 2
 
0.4%
0.9209 2
 
0.4%
1.041 2
 
0.4%
1.216 2
 
0.4%
Other values (509) 545
95.8%
ValueCountFrequency (%)
0.3602 1
0.2%
0.3621 1
0.2%
0.3628 1
0.2%
0.3871 1
0.2%
0.3981 1
0.2%
0.4064 1
0.2%
0.4125 1
0.2%
0.4334 1
0.2%
0.4336 1
0.2%
0.4402 1
0.2%
ValueCountFrequency (%)
4.885 1
0.2%
3.896 1
0.2%
3.647 1
0.2%
3.568 1
0.2%
3.12 1
0.2%
2.927 1
0.2%
2.91 1
0.2%
2.904 1
0.2%
2.878 1
0.2%
2.836 1
0.2%

smoothness error
Real number (ℝ)

Distinct547
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0070409789
Minimum0.001713
Maximum0.03113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T19:10:11.281301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.001713
5-th percentile0.0036902
Q10.005169
median0.00638
Q30.008146
95-th percentile0.012644
Maximum0.03113
Range0.029417
Interquartile range (IQR)0.002977

Descriptive statistics

Standard deviation0.0030025179
Coefficient of variation (CV)0.42643473
Kurtosis10.46984
Mean0.0070409789
Median Absolute Deviation (MAD)0.001451
Skewness2.3144501
Sum4.006317
Variance9.015114 × 10-6
MonotonicityNot monotonic
2023-05-03T19:10:11.554044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.006399 2
 
0.4%
0.01052 2
 
0.4%
0.01291 2
 
0.4%
0.007189 2
 
0.4%
0.005298 2
 
0.4%
0.01038 2
 
0.4%
0.007595 2
 
0.4%
0.007803 2
 
0.4%
0.00508 2
 
0.4%
0.00604 2
 
0.4%
Other values (537) 549
96.5%
ValueCountFrequency (%)
0.001713 1
0.2%
0.002667 1
0.2%
0.002826 1
0.2%
0.002838 1
0.2%
0.002866 1
0.2%
0.002887 1
0.2%
0.003139 1
0.2%
0.003169 1
0.2%
0.003245 1
0.2%
0.003265 1
0.2%
ValueCountFrequency (%)
0.03113 1
0.2%
0.02333 1
0.2%
0.02177 1
0.2%
0.02075 1
0.2%
0.01835 1
0.2%
0.01736 1
0.2%
0.01721 1
0.2%
0.01604 1
0.2%
0.01582 1
0.2%
0.01574 1
0.2%

compactness error
Real number (ℝ)

Distinct541
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.025478139
Minimum0.002252
Maximum0.1354
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T19:10:11.831273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.002252
5-th percentile0.0078922
Q10.01308
median0.02045
Q30.03245
95-th percentile0.060578
Maximum0.1354
Range0.133148
Interquartile range (IQR)0.01937

Descriptive statistics

Standard deviation0.017908179
Coefficient of variation (CV)0.70288413
Kurtosis5.1062525
Mean0.025478139
Median Absolute Deviation (MAD)0.00876
Skewness1.9022207
Sum14.497061
Variance0.00032070289
MonotonicityNot monotonic
2023-05-03T19:10:12.134099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01812 3
 
0.5%
0.0231 3
 
0.5%
0.01104 3
 
0.5%
0.02431 2
 
0.4%
0.01203 2
 
0.4%
0.03055 2
 
0.4%
0.0118 2
 
0.4%
0.01395 2
 
0.4%
0.01646 2
 
0.4%
0.02219 2
 
0.4%
Other values (531) 546
96.0%
ValueCountFrequency (%)
0.002252 1
0.2%
0.003012 1
0.2%
0.00371 1
0.2%
0.003746 1
0.2%
0.00466 1
0.2%
0.004693 1
0.2%
0.004711 1
0.2%
0.004883 1
0.2%
0.004899 1
0.2%
0.00493 1
0.2%
ValueCountFrequency (%)
0.1354 1
0.2%
0.1064 1
0.2%
0.1006 1
0.2%
0.09806 1
0.2%
0.09586 1
0.2%
0.09368 1
0.2%
0.08808 1
0.2%
0.08668 1
0.2%
0.08606 1
0.2%
0.08555 1
0.2%

concave points error
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct507
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.011796137
Minimum0
Maximum0.05279
Zeros13
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T19:10:12.423671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0038308
Q10.007638
median0.01093
Q30.01471
95-th percentile0.022884
Maximum0.05279
Range0.05279
Interquartile range (IQR)0.007072

Descriptive statistics

Standard deviation0.0061702852
Coefficient of variation (CV)0.52307676
Kurtosis5.1263019
Mean0.011796137
Median Absolute Deviation (MAD)0.003485
Skewness1.4446781
Sum6.712002
Variance3.8072419 × 10-5
MonotonicityNot monotonic
2023-05-03T19:10:12.718076image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
2.3%
0.01167 3
 
0.5%
0.0111 3
 
0.5%
0.01499 3
 
0.5%
0.01004 2
 
0.4%
0.01067 2
 
0.4%
0.005495 2
 
0.4%
0.01011 2
 
0.4%
0.01196 2
 
0.4%
0.01155 2
 
0.4%
Other values (497) 535
94.0%
ValueCountFrequency (%)
0 13
2.3%
0.001852 1
 
0.2%
0.002386 1
 
0.2%
0.002404 1
 
0.2%
0.002924 1
 
0.2%
0.002941 1
 
0.2%
0.003125 1
 
0.2%
0.003242 1
 
0.2%
0.003333 1
 
0.2%
0.00339 1
 
0.2%
ValueCountFrequency (%)
0.05279 1
0.2%
0.0409 1
0.2%
0.03927 1
0.2%
0.03487 1
0.2%
0.03441 1
0.2%
0.03322 1
0.2%
0.03024 1
0.2%
0.02919 1
0.2%
0.02853 1
0.2%
0.02801 1
0.2%

symmetry error
Real number (ℝ)

Distinct498
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.020542299
Minimum0.007882
Maximum0.07895
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T19:10:13.003375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.007882
5-th percentile0.011758
Q10.01516
median0.01873
Q30.02348
95-th percentile0.034988
Maximum0.07895
Range0.071068
Interquartile range (IQR)0.00832

Descriptive statistics

Standard deviation0.0082663715
Coefficient of variation (CV)0.40240733
Kurtosis7.8961298
Mean0.020542299
Median Absolute Deviation (MAD)0.00393
Skewness2.1951329
Sum11.688568
Variance6.8332898 × 10-5
MonotonicityNot monotonic
2023-05-03T19:10:13.276091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01344 4
 
0.7%
0.02045 3
 
0.5%
0.01884 3
 
0.5%
0.01647 3
 
0.5%
0.0187 3
 
0.5%
0.01897 3
 
0.5%
0.01454 3
 
0.5%
0.01536 3
 
0.5%
0.01924 3
 
0.5%
0.01263 2
 
0.4%
Other values (488) 539
94.7%
ValueCountFrequency (%)
0.007882 1
0.2%
0.009539 1
0.2%
0.009947 1
0.2%
0.01013 1
0.2%
0.01029 1
0.2%
0.01054 1
0.2%
0.01055 1
0.2%
0.01057 1
0.2%
0.01062 1
0.2%
0.01065 2
0.4%
ValueCountFrequency (%)
0.07895 1
0.2%
0.06146 1
0.2%
0.05963 1
0.2%
0.05628 1
0.2%
0.05543 1
0.2%
0.05333 1
0.2%
0.05168 1
0.2%
0.05113 1
0.2%
0.05014 1
0.2%
0.04783 1
0.2%

worst symmetry
Real number (ℝ)

Distinct500
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29007557
Minimum0.1565
Maximum0.6638
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-05-03T19:10:13.584297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1565
5-th percentile0.2127
Q10.2504
median0.2822
Q30.3179
95-th percentile0.40616
Maximum0.6638
Range0.5073
Interquartile range (IQR)0.0675

Descriptive statistics

Standard deviation0.061867468
Coefficient of variation (CV)0.21328052
Kurtosis4.4445595
Mean0.29007557
Median Absolute Deviation (MAD)0.0342
Skewness1.4339278
Sum165.053
Variance0.0038275835
MonotonicityNot monotonic
2023-05-03T19:10:13.852738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2226 3
 
0.5%
0.2369 3
 
0.5%
0.2972 3
 
0.5%
0.3196 3
 
0.5%
0.3109 3
 
0.5%
0.2383 3
 
0.5%
0.2268 2
 
0.4%
0.3103 2
 
0.4%
0.2576 2
 
0.4%
0.2849 2
 
0.4%
Other values (490) 543
95.4%
ValueCountFrequency (%)
0.1565 1
0.2%
0.1566 1
0.2%
0.1603 1
0.2%
0.1648 1
0.2%
0.1652 1
0.2%
0.1712 1
0.2%
0.1783 2
0.4%
0.1811 1
0.2%
0.1859 1
0.2%
0.189 1
0.2%
ValueCountFrequency (%)
0.6638 1
0.2%
0.5774 1
0.2%
0.5558 1
0.2%
0.544 1
0.2%
0.5166 1
0.2%
0.4882 1
0.2%
0.4863 1
0.2%
0.4824 1
0.2%
0.4761 1
0.2%
0.4753 1
0.2%

Interactions

2023-05-03T19:10:01.576677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:17.314743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:21.650922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:24.699911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:27.664649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:30.872899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:35.614094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:38.548737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:42.003865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:44.972740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:49.390419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:54.977453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:57.987912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:10:01.895590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:18.046497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:21.870305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:24.925617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:27.886605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:31.221860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:35.811124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:38.767457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:42.196588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:45.175238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:49.784326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:55.190367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:58.195216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:10:02.250921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:18.395765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:22.096366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:25.147238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:28.114605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:31.504688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:36.036747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:38.991775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:42.453392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:45.399008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:50.194499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:55.419528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:58.438545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:10:02.595274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:18.787454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:22.312140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:25.364328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:28.336579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:31.873068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:36.269511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:39.216917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:42.684887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:45.745322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:50.448584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:55.654310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:58.665534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:10:02.977017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:19.145878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:22.549800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:25.615234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:28.580096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:32.284855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:36.508061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:39.493251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:42.899007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:46.053101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:50.716089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:55.908361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:58.918509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:10:03.382275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:19.563094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:22.818377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:25.872847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:28.833340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:32.647884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:36.765687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:39.744900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:43.147706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:46.452076image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:50.972526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:56.151746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:59.165306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:10:03.749150image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:19.947320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:23.042595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:26.093141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:29.061799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:32.967582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:36.983681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:39.966778image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:43.386363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:46.791796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:51.210416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:56.365232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:59.384786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:10:04.144237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:20.324721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:23.282494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:26.310311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:29.284476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:33.335953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:37.228446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:40.186238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:43.618355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:47.171625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:51.449397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:56.595608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:59.636020image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:10:04.471121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:20.519192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:23.519221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:26.522793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:29.505253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:33.725323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:37.430972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:40.407094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:43.817013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:47.538917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:53.787076image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:56.810684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:59.874285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:10:04.851840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:20.761489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:23.762539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:26.755563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:29.736511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:34.120775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:37.664275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:40.656271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:44.038923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:47.913701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:54.016433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:57.047971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:10:00.126148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:10:05.165960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:20.990466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:24.007926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:27.001986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:29.994860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:34.512513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:37.891900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:41.063803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:44.277251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:48.234375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:54.249316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:57.282024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:10:00.441196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:10:05.377314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:21.200794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:24.237072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:27.219914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:30.218944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:34.891754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:38.106948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:41.502979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:44.508041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:48.628716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:54.497475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:57.520561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:10:00.789840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:10:05.618054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:21.442918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:24.476360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:27.453603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:30.537493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:35.331870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:38.349221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:41.780126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:44.759950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:49.051075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:54.747597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:09:57.769776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-03T19:10:01.211627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-03T19:10:14.084157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
mean radiusmean texturemean smoothnessmean compactnessmean symmetrymean fractal dimensionradius errortexture errorsmoothness errorcompactness errorconcave points errorsymmetry errorworst symmetry
mean radius1.0000.3410.1490.4980.120-0.3500.550-0.144-0.3260.2650.411-0.2410.175
mean texture0.3411.0000.0250.2660.110-0.0590.3640.4510.0370.2640.2390.0090.121
mean smoothness0.1490.0251.0000.6790.5420.5880.3340.0910.3390.3920.4390.1510.394
mean compactness0.4980.2660.6791.0000.5520.4990.5070.0480.1270.8180.7320.0980.450
mean symmetry0.1200.1100.5420.5521.0000.4280.3380.1390.2060.4360.3830.3840.710
mean fractal dimension-0.350-0.0590.5880.4990.4281.0000.0010.1570.4020.4810.2860.3140.295
radius error0.5500.3640.3340.5070.3380.0011.0000.3100.2230.4280.5960.2400.147
texture error-0.1440.4510.0910.0480.1390.1570.3101.0000.4440.2300.2610.389-0.120
smoothness error-0.3260.0370.3390.1270.2060.4020.2230.4441.0000.2850.3450.474-0.067
compactness error0.2650.2640.3920.8180.4360.4810.4280.2300.2851.0000.7640.2730.266
concave points error0.4110.2390.4390.7320.3830.2860.5960.2610.3450.7641.0000.2620.133
symmetry error-0.2410.0090.1510.0980.3840.3140.2400.3890.4740.2730.2621.0000.283
worst symmetry0.1750.1210.3940.4500.7100.2950.147-0.120-0.0670.2660.1330.2831.000

Missing values

2023-05-03T19:10:05.941971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-03T19:10:06.406847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

mean radiusmean texturemean smoothnessmean compactnessmean symmetrymean fractal dimensionradius errortexture errorsmoothness errorcompactness errorconcave points errorsymmetry errorworst symmetry
017.9910.380.118400.277600.24190.078711.09500.90530.0063990.049040.015870.030030.4601
120.5717.770.084740.078640.18120.056670.54350.73390.0052250.013080.013400.013890.2750
219.6921.250.109600.159900.20690.059990.74560.78690.0061500.040060.020580.022500.3613
311.4220.380.142500.283900.25970.097440.49561.15600.0091100.074580.018670.059630.6638
420.2914.340.100300.132800.18090.058830.75720.78130.0114900.024610.018850.017560.2364
512.4515.700.127800.170000.20870.076130.33450.89020.0075100.033450.011370.021650.3985
618.2519.980.094630.109000.17940.057420.44670.77320.0043140.013820.010390.013690.3063
713.7120.830.118900.164500.21960.074510.58351.37700.0088050.030290.014480.014860.3196
813.0021.820.127300.193200.23500.073890.30631.00200.0057310.035020.012260.021430.4378
912.4624.040.118600.239600.20300.082430.29761.59900.0071490.072170.014320.017890.4366
mean radiusmean texturemean smoothnessmean compactnessmean symmetrymean fractal dimensionradius errortexture errorsmoothness errorcompactness errorconcave points errorsymmetry errorworst symmetry
55911.5123.930.092610.102100.13880.065700.23882.9040.0082000.0298200.012670.014880.2112
56014.0527.150.099290.112600.15370.061710.36451.4920.0072560.0267800.016260.020800.2250
56111.2029.370.074490.035580.10600.055020.31413.8960.0075940.0088780.000000.019890.1566
56215.2230.620.104800.208700.21280.071520.26021.2050.0046250.0484400.016080.021370.4089
56320.9225.090.109900.223600.21490.068790.96221.0260.0063990.0431000.026240.020570.2929
56421.5622.390.111000.115900.17260.056231.17601.2560.0103000.0289100.024540.011140.2060
56520.1328.250.097800.103400.17520.055330.76552.4630.0057690.0242300.016780.018980.2572
56616.6028.080.084550.102300.15900.056480.45641.0750.0059030.0373100.015570.013180.2218
56720.6029.330.117800.277000.23970.070160.72601.5950.0065220.0615800.016640.023240.4087
5687.7624.540.052630.043620.15870.058840.38571.4280.0071890.0046600.000000.026760.2871